Prior works [1]}, [2]} typically input the table directly into a language model, expecting that the skill of querying the table will be naturally learned via the standard language modeling objective. We found this approach to under-perform in our experiments. These findings are also consistent with recent works which show that pre-training transformers for querying tables can significantly improve the transformer's performance on downstream tasks which use tables [3]}. AirConcierge [4]} takes a different approach, and explicitly predicts and executes SQL queries based on the dialogue. This approach obtains the SOTA task success on AirDialogue, but it involves several complex components, requires the ability to preform semantic parsing on the dialogue, and of course requires additional domain knowledge about the format and structure of the flight table, which reprsents the task context. In our work, we show that applying CALM for AirDialogue can close this gap by inducing task learning from language models and achieve end-to-end learning from the flight table, without sacrificing the generated language quality.
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